Project specifications
Outline
Describe the underlying patient demographics within out mitigable-activity sub-cohorts by:
- Age and sex
- Ethnicity and deprivation
Present activity totals and time-series trends for the combined mitigable-activity cohort and mitigator sub-cohorts.
Measures include:
- Counts of patients, admissions and bed days:
- In combined mitigable-activity cohort
- By mitigator sub-cohorts
- By ICB
- Cohort activity as a share of all admissions
Explore variation in age- and sex-adjusted admission rates to:
- Examine the range of activity by ICB
- Assess whether systems in place are particularly good or bad at treating mitigable activity
Additional analysis: Incorporate underlying population need and/or disease prevalence (weighting by deprivation or regression controlling for population variables)
Apply survival analysis techniques to assess differences in care outcomes by patient groups and ICBs. Care outcomes include:
- Readmission post-acute inpatient care
- Motality post-acute inpatient care
Additional analysis:
- Summary of care services/types that patients from mitigable cohorts are in contact with in the year prior to death.
- Comparison of location of death in mitigable patients that recieved acute inpatient care in the year prior to death.
Display the overlap of patients in mitigable cohorts by combined and sub-cohorts.
Outputs include:
- Underlying SQL queries
- Code
- Data extract
- Quarto report output
Ranked table of activity in each mitigator with activity counts and proportion of activity
Patient characteristics
Demographics
In both our emergency elderly and frail patient groups, it is apparent that within the Black and Asian ethnic groups, the majority of patients are clustered in the most deprived IMD deciles (particularly deciles 2-4). However, when considering emergency elderly admissions, in larger group of White British patients, the proportion of patients gradually increases with affluence.
Most frequent diagnoses
Most frequent procedures
Size and describe
The following section will describe mitigable-activity in our combined cohort and mitigator-specific sub-cohorts.
We have created our data set by applying existing strategies developed under the New Hospitals Program (NHP) that filter patient-level hospital activity data (Secondary Uses Service data - SUS) which center on patient groups and pathways which might reasonably be suitable for treatment in the community.
The following patient groups have been identified with such mitigation strategies:
- Admissions in frail elderly patients (65 years +)
- Emergency admissions in over 75’s
- End-of-life admissions: spells that ended with the patient dying and lasted less that 14 days
- Admissions post-falls (slips, trips and falls)
Combined mitigable activity cohort
Activity in the combined data set has gradually returned to pre-pandemic levels. While the increase in post-pandemic growth in spells has been gradual between 2021 and 2024, the length of time a person from our cohort has stayed in hospital has risen quickly to surpass pre-pandemic levels. The graph above suggests a change in the average length of stay particularly between 2021 and 2023, after which bed-days associated with our activity have plateaued.
Is the change in bed days linked to an easing of pandemic-related practices around length of treatment and/or discharge?
We contextualise the activity identified in our cohort alongside wider NHS-funded healthcare delivery. The combined sum of admissions from our emergency eldery, frail, falls and end-of-life cohorts accounts to approx 11% of total admissions. The trend varied most significantly during the COVID-19 pandemic and has since returned to pre-pandemic levels, allowing for consistent seasonal peaked during the winter months.
Sub-cohorts
By splitting our activity according to mitigation strategy, we can compare trends in patient group. The volume of activity by cohort differs significantly with Emergency Elderly and Frail activity accounting for the larger shares of total admissions and bed days.
Though the post-pandemic trend in frail activity is growing at a steady rate (from c.90,000 per month in 2022 up to c.100,000 per month since), there has been a recent stepped increase in emergency elderly admissions in the last year (stable around 140,000 per month between 2021-23 but increased to 160,000 since start of 2024). For both of these cohorts, the length of stay associated with these admissions has grown substantially and is higher than pre-pandemic levels in the emergency elderly cohort.
Similarly, the length of stay associated with falls admissions has surged while the underlying activity has remained stable.
Excluding pandemic related surges in end of life care in secondary settings, trends in activity and bed days are stable and correlated.
When we separate our patient cohorts, we’re reminded of the comparative differences in scale. The emergency elderly and frail cohorts account for approximately 10 and 6.5 per cent of total inpatient hospital activity respectively, while falls and end of life care are both below 1% total admissions. All cohorts saw increases in proportion during the pandemic as activity in other patient groups reduced.
ICB trends
In absolute volume terms, ICB’s differ significantly in the number of mitigable activity undertaken in secondary care settings though this may largely be a function of differences in underlying population distribution of elderly people.
The trends in mitigable activity by ICB vary significantly.
Data quality issues are apparent in a handful of ICB’s:
- Frimley,
- Shropshire,
- Telford and Wrekin,
- Cambs & Peterborough and
- Dorset.
While some ICB’s display sustained reductions in mitigable admissions:
- South West London,
- West Yorkshire and
- Lincolnshire.
Others show significant growth in activity: - Devon, - Staffordshire & Stoke-on-Trent and - South Yorkshire for example.
Are boundary changes impactful here?
Share of total activity in ICB
Below we display the range of variation in the mitigable activity as a proportion of total admissions by ICB. Each boxplot represents monthly proportions and particularly high or low outlier ICB’s are identified.
Use variation
To account for differences in the underlying number of elderly patients in each ICB, we standardised admission rates by age and sex to assess activity by ICB, if the age and sex structure of each ICB mirrored that of England (mid 2023).
Admission rates range from 25-55 admissions per 1,000 population between our ICB’s during our data collection period. While the general trend in admission rates is increasing, there is significant variation between ICB’s over the last 5 years, having accounted for age and sex structure.
Admission rate by sub-cohort
The overall trends in Emergency elderly and Frail patients are similar as the frail cohort is largely a sub-set of the emergency elderly group; increasing admission rates are seen in the post-pandemic period in both. While the trend in falls admission rates is more stable, more statistical outliers exist. The trend in end of life admission rates is reducing post-pandemic but has not returned to pre-pandemic levels and considerable variation by ICB is present where it wasn’t before the pandemic.
To assess the geographic distribution of admission rates we split our data into quintiles. Quintile 1 includes the ICB’s with the lowest 20% admission rates, while quintile 5 includes ICB’s with the highest 20%.
Visually we can identify that ICB’s in the higher admission rate quintiles for emergency elderly and frail cohorts are similar and cluster in the northern and midlands geographies. Where are ICB’s with the highest falls admission rates are located around London and ICB’s most often utilising secondary settings for end of life care are seen in the east, around Bristol, Worcester and Birmingham.
Alternatively we can visualise admission rates in our sub-cohorts by ICB using the below bar chart.
Funnel plots
2x2 plots
Variation in outcomes
Furthering our assessment of variation in treatment of these mitigable patient populations, we analyse variation in selected patient outcomes; readmission and mortality.
This sub-strand of our descriptive analysis aims to investigate the risk of death or subsequent readmission in patients who have had an emergency hospital admission. We apply survival analysis techniques to help us understand what factors influence the risk of death or readmission, including determining variability between ICB area.
Full survival analysis output found here: https://the-strategy-unit.github.io/Community_Strategies/community_strategy_survival_analysis.html#readmissions
Mitigator overlap
Finally, explore the correlation and overlap between and within the cohorts included within the Community Strategies analysis.
Full analysis on overlap found here: https://the-strategy-unit.github.io/Community_Strategies/identifying_overlap_between_cohorts.html
Contact
If you have any questions or comments regarding any of the above analysis please email strategy.unit@nhs.net citing the NHSE Community Services Analysis.